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Chapter 7. Control Charts for AttributesPowerPoint Presentation

Chapter 7. Control Charts for Attributes

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Chapter 7. Control Charts for Attributes

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Control Chart for Fraction Nonconforming

Fraction nonconforming is based on the binomial distribution.

n: size of population

p: probability of nonconformance

D: number of products not conforming

Successive products are independent.

Mean of D = np

Variance of D = np(1-p)

w: statistics for quality

Mean of w: μw

Variance of w: σw2

L: distance of control limit from center line (in standard deviation units)

If p is the true fraction nonconformance:

If p is not know, we estimate it from samples.

m: samples, each with n units (or observations)

Di: number of nonconforming units in sample i

Average of all observations:

Control chart variables using only the recent 24 samples: chart:

Set equal to zero for negative value

Design of Fraction Nonconforming Chart chart:

Three parameters to be specified:

- sample size
- frequency of sampling
- width of control limits
Common to base chart on 100% inspection of all process output over time.

Rational subgroups may also play role in determining sampling frequency.

np chart: Control Chart

Variable Sample Size chart:

Variable-Width Control Limits

Variable Sample Size chart:

Control Limits Based on an Average Sample Size

Use average sample size. For previous example:

Variable Sample Size chart:

Standard Control Chart

- Points are plotted in standard deviation units.

UCL = 3

Center line = 0

LCL = -3

Operating Characteristic Function and Average Run Length Calculations

Probability of type II error

Since CalculationsD is an integer,

Control Charts for Nonconformities (or Defects) Calculations

Procedures with Constant Sample Size

x: number of nonconformities

c > 0: parameter of Poisson distribution

Set to zero if negative

If no standard is given, estimate c then use the following parameters:

Set to zero if negative

There are 516 defects in total of 26 samples. Thus. parameters:

Sample 6 was due to inspection error.

Sample 20 was due to a problem in wave soldering machine.

Eliminate these two samples, and recalculate the control parameters.

New control limits:

Additional samples collected. parameters:

Further Analysis of Nonconformities parameters:

Choice of Sample Size: parameters:μ Chart

x: total nonconformities in n inspection units

u: average number of nonconformities per inspection unit

Control Charts for Nonconformities parameters:

Procedure with Variable Sample Size

Control Charts for Nonconformities parameters:

Demerit Systems: not all defects are of equal importance

c parameters:iA: number of Class A defects in ith inspection units

Similarly for ciB, ciC, and ciD for Classes B, C, and D.

di: number of demerits in inspection unit i

Constants 100, 50, 10, and 1 are demerit weights.

µ parameters:i: linear combination of independent Poisson variables

Control Charts for Nonconformities parameters:

Operating Characteristic Function

x: Poisson random variable

c: true mean value

β: type II error probability

For example 6-3 parameters:

Number of nonconformities is integer.

Control Charts for Nonconformities parameters:

Dealing with Low Defect Levels

- If defect level is low, <1000 per million, c and u charts become ineffective.
- The time-between-events control chart is more effective.
- If the defects occur according to a Poisson distribution, the probability distribution of the time between events is the exponential distribution.
- Constructing a time-between-events control chart is essentially equivalent to control charting an exponentially distributed variable.
- To use normal approximation, translate exponential distribution to Weibull distribution and then approximate with normal variable

Guidelines for Implementing Control Charts parameters:

Applicable for both variable and attribute control

Determining Which Characteristics and parameters:

Where to Put Control Charts

Choosing Proper Type of Control Chart parameters:

Actions Taken to Improve Process parameters: